NVIDIA Tesla creates the personal supercomputer

NVIDIA announced the availability of the GPU-based Tesla Personal Supercomputer, these systems feature up to four Tesla C1060 graphics cards with 4GB GDDR3 memory each and can deliver a single-precision computing power of 4 teraFLOPS for under $10,000! System builders can compose their own systems, but NVIDIA recommends a quad-core processor as well as 16GB of memory. These computing systems are aimed at researchers who need massive parallel computing power, they deliver the equivalent computing power of a cluster, at 1/100th of the price and in a form factor of a standard desktop workstation. Not only are they a lot cheaper, but they also generate a lot less heat and consume a lot less power.

The Tesla Personal Supercomputer doesn't make supercomputing clusters obsolete but it's a major breakthrough for millions of researchers who can take advantage of the huge heterogeneous computing power of this system. NVIDIA estimates this affordable supercomputer has a target market of over 15 million researchers globally. For some more information about what these systems can do I suggest you read the article I published about the FASTRA supercomputing. Researchers at the University of Antwerp created this system with four off-the-shelf GeForce 9800 GX2 graphics cards, it cost them less than 4000EUR to build and it beats a 256-node supercomputer cluster with dual AMD Opteron 250 2.4GHz chips that cost 3.5 million EUR in March 2005.

"We've all heard 'desktop supercomputer' claims in the past, but this time it's for real," said Burton Smith, Microsoft Technical Fellow. "NVIDIA and its partners will be delivering outstanding performance and broad applicability to the mainstream marketplace. Heterogeneous computing, where GPUs work in tandem with CPUs, is what makes such a breakthrough possible."

Priced like a conventional PC workstation, yet delivering 250 times the processing power, researchers now have the horsepower to perform complex, data-intensive computations right at their desk, processing more data faster and cutting time to discovery.

"GPUs have evolved to the point where many real world applications are easily implemented on them and run significantly faster than on multi-core systems," said Prof. Jack Dongarra, director of the Innovative Computing Laboratory at the University of Tennessee and author of LINPACK. "Future computing architectures will be hybrid systems with parallel-core GPUs working in tandem with multi-core CPUs."

Leading institutions including MIT, the Max Planck Institute, University of Illinois at Urbana-Champaign, Cambridge University, and others are already advancing their research using GPU-based personal supercomputers.

"GPU based systems enable us to run life science codes in minutes rather than the hours it took earlier. This exceptional speedup has the ability to accelerate the discovery of potentially life-saving anti-cancer drugs," said Jack Collins, manager of scientific computing and program development at the Advanced Biomedical Computing Center in Frederick Md., operated by SAIC-Frederick, Inc.

At the core of the GPU-based Tesla Personal Supercomputer is the Tesla C1060 GPU Computing Processor which is based on the NVIDIA(R) CUDA(TM) parallel computing architecture. CUDA enables developers and researchers to harness the massively parallel computational power of Tesla through industry standard C.

"Dell has led the workstation category for almost a decade and GPU computing represents a massive leap forward in performance that will bring supercomputer power to the masses," said Antonio Julio, director, Dell Product Group. "The Dell Precision R5400 and T7400 will allow the scientific community to harness the capabilities of the NVIDIA Tesla C1060 GPU with up to two teraflops of computational power."